"""simple docstring"""



import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Optional[int]									=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE__:List[Any]									=			[]
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
   )
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
   rename_keys.append(
       (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
   )
   rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
   )
   rename_keys.append(
       (
           F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
           F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
       )
   )
   rename_keys.append(
       (
           F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
           F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
       )
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
   )

   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    state_dict.pop(a				)
       __a       =    val



def 	_lowerCamelCase(	a				):
       __a       =    OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     __a       =    key.replace("backbone.0.body"   ,    "backbone.conv_encoder.model"				)
                     __a       =    value
              else:
                     __a       =    value

       return new_state_dict



def 	_lowerCamelCase(	a   ,    a=False				):
       __a       =    ""
       if is_panoptic:
              __a       =    "conditional_detr."

       # first: transformer encoder
       for i in range(6				):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              __a       =    state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"				)
              __a       =    state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"				)
              # next, add query, keys and values (in that order) to the state dict
              __a       =    in_proj_weight[:2_5_6, :]
              __a       =    in_proj_bias[:2_5_6]
              __a       =    in_proj_weight[2_5_6:5_1_2, :]
              __a       =    in_proj_bias[2_5_6:5_1_2]
              __a       =    in_proj_weight[-2_5_6:, :]
              __a       =    in_proj_bias[-2_5_6:]



def 	_lowerCamelCase(	):
       __a       =    "http://images.cocodataset.org/val2017/000000039769.jpg"
       __a       =    Image.open(requests.get(a   ,    stream=a				).raw				)

       return im



@torch.no_grad()
def 	_lowerCamelCase(	a   ,    a				):
       __a       =    ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              __a       =    "resnet101"
       if "dc5" in model_name:
              __a       =    True
       __a       =    "panoptic" in model_name
       if is_panoptic:
              __a       =    2_5_0
       else:
              __a       =    9_1
              __a       =    "huggingface/label-files"
              __a       =    "coco-detection-id2label.json"
              __a       =    json.load(open(hf_hub_download(a   ,    a   ,    repo_type="dataset"				)   ,    "r"				)				)
              __a       =    {int(a				): v for k, v in idalabel.items()}
              __a       =    idalabel
              __a       =    {v: k for k, v in idalabel.items()}

       # load image processor
       __a       =    "coco_panoptic" if is_panoptic else "coco_detection"
       __a       =    ConditionalDetrImageProcessor(format=a				)

       # prepare image
       __a       =    prepare_img()
       __a       =    image_processor(images=a   ,    return_tensors="pt"				)
       __a       =    encoding["pixel_values"]

       logger.info(F"Converting model {model_name}..."				)

       # load original model from torch hub
       __a       =    torch.hub.load("DeppMeng/ConditionalDETR"   ,    a   ,    pretrained=a				).eval()
       __a       =    conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     __a       =    "conditional_detr." + src
              rename_key(a   ,    a   ,    a				)
       __a       =    rename_backbone_keys(a				)
       # query, key and value matrices need special treatment
       read_in_q_k_v(a   ,    is_panoptic=a				)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       __a       =    "conditional_detr.model." if is_panoptic else "model."
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("conditional_detr"				)
                         and not key.startswith("class_labels_classifier"				)
                         and not key.startswith("bbox_predictor"				)
                     ):
                            __a       =    state_dict.pop(a				)
                            __a       =    val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            __a       =    state_dict.pop(a				)
                            __a       =    val
                     elif key.startswith("bbox_attention"				) or key.startswith("mask_head"				):
                            continue
                     else:
                            __a       =    state_dict.pop(a				)
                            __a       =    val
              else:
                     if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
                            __a       =    state_dict.pop(a				)
                            __a       =    val
    # finally, create HuggingFace model and load state dict
       __a       =    ConditionalDetrForSegmentation(a				) if is_panoptic else ConditionalDetrForObjectDetection(a				)
       model.load_state_dict(a				)
       model.eval()
       model.push_to_hub(repo_id=a   ,    organization="DepuMeng"   ,    commit_message="Add model"				)
       # verify our conversion
       __a       =    conditional_detr(a				)
       __a       =    model(a				)
       assert torch.allclose(outputs.logits   ,    original_outputs["pred_logits"]   ,    atol=1E-4				)
       assert torch.allclose(outputs.pred_boxes   ,    original_outputs["pred_boxes"]   ,    atol=1E-4				)
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks   ,    original_outputs["pred_masks"]   ,    atol=1E-4				)

       # Save model and image processor
       logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."				)
       Path(a				).mkdir(exist_ok=a				)
       model.save_pretrained(a				)
       image_processor.save_pretrained(a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:int									=			argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""",
       default="""conditional_detr_resnet50""",
       type=str,
       help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""



import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
            "unet/diffusion_pytorch_model.bin",
            # Removed: 'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertFalse(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
        ]
        self.assertTrue(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.bin",
            "safety_checker/model.safetensors",
            "vae/diffusion_pytorch_model.bin",
            "vae/diffusion_pytorch_model.safetensors",
            "text_encoder/pytorch_model.bin",
            # Removed: 'text_encoder/model.safetensors',
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        self.assertFalse(is_safetensors_compatible(lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        # pass variant but use the non-variant filenames
        __a       =    [
            "unet/diffusion_pytorch_model.bin",
            "unet/diffusion_pytorch_model.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
            "unet/diffusion_pytorch_model.fp16.bin",
            # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        __a       =    "fp16"
        self.assertFalse(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        __a       =    [
            "text_encoder/pytorch_model.fp16.bin",
            "text_encoder/model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)


 def 						a__   ( self						):
        # pass variant but use the non-variant filenames
        __a       =    [
            "text_encoder/pytorch_model.bin",
            "text_encoder/model.safetensors",
        ]
        __a       =    "fp16"
        self.assertTrue(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)






 def 						a__   ( self						):
        __a       =    [
            "safety_checker/pytorch_model.fp16.bin",
            "safety_checker/model.fp16.safetensors",
            "vae/diffusion_pytorch_model.fp16.bin",
            "vae/diffusion_pytorch_model.fp16.safetensors",
            "text_encoder/pytorch_model.fp16.bin",
            # 'text_encoder/model.fp16.safetensors',
            "unet/diffusion_pytorch_model.fp16.bin",
            "unet/diffusion_pytorch_model.fp16.safetensors",
        ]
        __a       =    "fp16"
        self.assertFalse(is_safetensors_compatible(lowerCamelCase    ,		variant=lowerCamelCase						)						)
